An asset of the approach presented in [1]} is that it rests on a greedy search algorithm to determine the minimum loss clustering of (REF ).
Starting from the MCMC output, this greedy approach explores the space of partitions and is not restricted to those visited by the MCMC chain to find the optimum.
We include the possibility to estimate the optimal clustering using both the \(\mathcal {VI} \)  loss and Binder's loss, along with other loss functions, within BNPdensity by adding an optional dependence to GreedyEPL.
Note that clustering estimation is also available for censored data, although graphical representation is more tricky (see also the legend to fig:clusteringCarbarylcens).
